Organizations are adopting AI in hiring to solve scale, speed, and consistency problems. The promise is straightforward: reduce bias, process more candidates, and make better decisions using data (Rigotti et al., 2024; Fernandez-Mateo, 2025). In practice, the opposite is happening. AI is increasingly being placed at the front of the hiring process, where information is least complete and judgment matters most. Systems are asked to screen, rank, or exclude candidates before any human has meaningfully engaged with them. Humans are then brought in later to review outputs, explain outcomes, or justify decisions already made (Fernandez-Mateo, 2025). That placement error is the core problem. Hiring isn't primarily a data classification task....